Topic: M4D, an Open Source Research CFD Code for the Calculation of Classical and Turbulent/Transitional Flows

Date: Tuesday, August 4, 2015

Time: 11:00am-noon (EST)

Room: NIA, Rm137

Speaker: Joan G. Moore

Speaker Bio: It is fifty years since Joan and John Moore met in M.I.T.’s Gas Turbine Laboratory. John had come with a B.Sc. (Eng.) in Mechanical Engineering from Imperial College, London to obtain an S.M. and then an Sc.D from M.I.T. Joan with a B.S. in Applied Mathematics from M.I.T., had the job of writing computer codes and helping Graduate students with theirs. Thus began a life-long CFD and turbulence modeling collaboration. John is currently a Professor Emeritus of Mechanical Engineering at Virginia Tech. Their first ‘retirement’ project, their book “Functional Reynolds Stress Modeling” was published in 2006. And now Joan has written her 4th CFD code, M4D, but her first one unfettered by external sponsorship.

Abstract: M4D features unsteady convection adapted control volumes and the MARV/MARVS Reynolds stress models. Convection adapted control volumes are a paradigm shift in CFD. Used with tri-linear discretization of convected properties in space over a fixed grid (formally 3-d 2nd-order accurate), they provide a balance between accuracy and stability not found in fixed volume methods. The MARV/MARVS Reynolds stress models are advanced turbulence models which calculate transition naturally based on an understanding of homogeneous shear flow at high dimensionless strain rates.
The presentation will concentrate on the convection adapted control volumes – the method, the combination of stability and accuracy, with the examples of an inviscid Kelvin-Helmholtz shear layer instability and near-DNS of flow in a square channel. The steady flow Reynolds stress model examples of transitional flow and heat transfer in a turbine cascade (Butler et al.) and of a backward facing step (Kasagi) also use the convection adapted control volumes.

Additional information, including the webcast link, can be found at the NIA CFD Seminar website, which is temporarily located at

Speaker Bio: Graeme Kennedy is an Assistant Professor in the School of Aerospace Engineering at the Georgia Institute of Technology where he leads his research group focused on developing novel design optimization methods for structural and multidisciplinary aerospace systems. Before joining the Georgia Tech faculty, he worked as a Postdoctoral Research Fellow at the University of Michigan in the Multidisciplinary Design Optimization lab. He received his Ph.D. from the University of Toronto Institute for Aerospace Studies (UTIAS) under the supervision of Prof. Joaquim R.R.A. Martins in 2012 and his M.A.Sc. from UTIAS under the supervision of Prof. Jorn Hansen in 2007. He received his undergraduate degree in Aerospace Engineering from the University of Toronto in 2005. A complete list of papers and ongoing projects is available on Dr. Kennedy’s website: http://gkennedy.gatech.edu/.

Abstract: Additive manufacturing methods give engineers greater freedom to design structures with fewer geometric and processing constraints than conventional manufacturing methods. Additive manufacturing, therefore, has the potential to enable the design and production of low-weight high-performance structures. However, optimization of additively-manufactured structures using conventional optimization techniques, such as topology optimization, is challenging due to the demanding mesh requirements and large size of the design problem. In this presentation, these difficulties will be addressed through scalable methods for large-scale analysis and optimization. The proposed approach utilizes a multigrid-preconditioned Krylov method for solving large structural finite-element problems coupled with a parallel interior-point method for large-scale constrained optimization. The proposed method will be demonstrated on a large-scale mass-constrained compliance minimization problem for a structure discretized using a 64 × 64 × 256 element mesh, resulting in 3.26 million structural degrees of freedom, 5.24 million design variables and 1.05 million linear constraints.

Additional information, including the webcast link, can be found at the NIA CFD Seminar website, which is temporarily located at